Citation

Necula E. Transp. Res. Proc. 2015; 10: 276-285.

Copyright

(Copyright © 2015, Elsevier Publications)

DOI

10.1016/j.trpro.2015.09.077

PMID

unavailable

Abstract

Nowadays GPS enabled devices are widely spread between drivers making the collection of GPS data more accessible. So there is an opportunity to infer useful patterns and trends. In this research, we plan to apply a statistical approach on 10000 vehicle GPS traces, from around 3600 drivers which are mined to extract the outlier traffic pattern to be used further in an Intelligent Transportation System. We choose to divide the urban area into a grid and organizing the road infrastructure as segments in a graph. Further, at a given time we can make an assumption regarding the congestion level in a specific area taking into account the visits for each vehicle, using the GPS trace data. Over time, the visited segments will settle into a pattern and vary periodically. In this study we will use R software in conjunction with a set of libraries. They provide an environment in which we can perform statistical analysis and produce graphics to annotate different results. Our objective is to identify contiguous set of road segments and time intervals which have the largest statistically significant relevance in forming traffic patterns. Taking into account the number of drivers that submitted their routes in correlation with the entire population on New Haven we can state that a 2-3% penetration rate of smart phones is enough to provide accurate measurements of the traffic flow and identification of traffic patterns.

Language: en

Keywords

car mobility; congestion; GPS patterns; R-language; traffic flow